Submit a custom JAR step - Amazon EMR
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Submit a custom JAR step

A custom JAR runs a compiled Java program that you can upload to Amazon S3. You should compile the program against the version of Hadoop you want to launch, and submit a CUSTOM_JAR step to your Amazon EMR cluster. For more information about how to compile a JAR file, see Build binaries using Amazon EMR.

For more information about building a Hadoop MapReduce application, see the MapReduce Tutorial in the Apache Hadoop documentation.

This section covers the basics of submitting a custom JAR step in Amazon EMR. Submitting a custom JAR step enables you to write a script to process your data with the Java programming language.

Submit a custom JAR step with the console

This example describes how to use the Amazon EMR console to submit a custom JAR step to a running cluster.

To submit a custom JAR step with the console
  1. Open the Amazon EMR console at

  2. In the Cluster List, select the name of your cluster.

  3. Scroll to the Steps section and expand it, then choose Add step.

  4. In the Add Step dialog:

    • For Step type, choose Custom JAR.

    • For Name, accept the default name (Custom JAR) or type a new name.

    • For JAR S3 location, type or browse to the location of your JAR file. JAR location maybe a path into S3 or a fully qualified java class in the classpath..

    • For Arguments, type any required arguments as space-separated strings or leave the field blank.

    • For Action on failure, accept the default option (Continue).

  5. Choose Add. The step appears in the console with a status of Pending.

  6. The status of the step changes from Pending to Running to Completed as the step runs. To update the status, choose the Refresh icon above the Actions column.

Launching a cluster and submitting a custom JAR step with the Amazon CLI

To launch a cluster and submit a custom JAR step with the Amazon CLI

To launch a cluster and submit a custom JAR step with the Amazon CLI, type the create-cluster subcommand with the --steps parameter.

  • To launch a cluster and submit a custom JAR step, type the following command, replace myKey with the name of your EC2 key pair, and replace mybucket with your bucket name.

    aws emr create-cluster --name "Test cluster" --release-label emr-7.0.0 \ --applications Name=Hue Name=Hive Name=Pig --use-default-roles \ --ec2-attributes KeyName=myKey --instance-type m5.xlarge --instance-count 3 \ --steps Type=CUSTOM_JAR,Name="Custom JAR Step",ActionOnFailure=CONTINUE,Jar=pathtojarfile,Args=["pathtoinputdata","pathtooutputbucket","arg1","arg2"]

    Linux line continuation characters (\) are included for readability. They can be removed or used in Linux commands. For Windows, remove them or replace with a caret (^).

    When you specify the instance count without the --instance-groups parameter, a single primary node launches, and the remaining instances launch as core nodes. All nodes use the instance type that you specify in the command.


    If you have not previously created the default Amazon EMR service role and EC2 instance profile, type aws emr create-default-roles to create them before typing the create-cluster subcommand.

    For more information on using Amazon EMR commands in the Amazon CLI, see

Third-party dependencies

Sometimes it may be necessary to include in the MapReduce classpath JARs for use with your program. You have two options for doing this:

  • Include the --libjars s3://URI_to_JAR in the step options for the procedure in Launching a cluster and submitting a custom JAR step with the Amazon CLI.

  • Launch the cluster with a modified mapreduce.application.classpath setting in mapred-site.xml. Use the mapred-site configuration classification. To create the cluster with the step using Amazon CLI, this would look like the following:

    aws emr create-cluster --release-label emr-7.0.0 \ --applications Name=Hue Name=Hive Name=Pig --use-default-roles \ --instance-type m5.xlarge --instance-count 2 --ec2-attributes KeyName=myKey \ --steps Type=CUSTOM_JAR,Name="Custom JAR Step",ActionOnFailure=CONTINUE,Jar=pathtojarfile,Args=["pathtoinputdata","pathtooutputbucket","arg1","arg2"] \ --configurations


    [ { "Classification": "mapred-site", "Properties": { "mapreduce.application.classpath": "path1,path2" } } ]

    The comma-separated list of paths should be appended to the JVM classpath for each task.